A few days ago I found on the page Jeff Rosenthal has dedicated to Hastings that he has passed away peacefully on May 13, 2016 in Victoria, British Columbia, where he lived for 45 years as a professor at the University of Victoria. After holding positions at University of Toronto, University of Canterbury (New Zealand), and Bell Labs (New Jersey). As pointed out by Jeff, Hastings’ main paper is his 1970 Biometrika description of Markov chain Monte Carlo methods, Monte Carlo sampling methods using Markov chains and their applications. Which would take close to twenty years to become known to the statistics world at large, although you can trace a path through Peskun (his only PhD student) , Besag and others. I am sorry it took so long to come to my knowledge and also sorry it apparently went unnoticed by most of the computational statistics community.

A book that came to me for review in CHANCE and that came completely unannounced is Andris Abakuks’ The Synoptic Problem and Statistics. “Unannounced” in that I had not heard so far of the synoptic problem. This problem is one of ordering and connecting the gospels in the New Testament, more precisely the “synoptic” gospels attributed to Mark, Matthew and Luke, since the fourth canonical gospel of John is considered by experts to be posterior to those three. By considering overlaps between those texts, some statistical inference can be conducted and the book covers (some of?) those statistical analyses for different orderings of ancestry in authorship. My overall reaction after a quick perusal of the book over breakfast (sharing bread and fish, of course!) was to wonder why there was no mention made of a more global if potentially impossible approach via a phylogeny tree considering the three (or more) gospels as current observations and tracing their unknown ancestry back just as in population genetics. Not because ABC could then be brought into the picture. Rather because it sounds to me (and to my complete lack of expertise in this field!) more realistic to postulate that those gospels were not written by a single person. Or at a single period in time. But rather that they evolve like genetic mutations across copies and transmission until they got a sort of official status.

“Given the notorious intractability of the synoptic problem and the number of different models that are still being advocated, none of them without its deficiencies in explaining the relationships between the synoptic gospels, it should not be surprising that we are unable to come up with more definitive conclusions.” (p.181)

The book by Abakuks goes instead through several modelling directions, from logistic regression using variable length Markov chains [to predict agreement between two of the three texts by regressing on earlier agreement] to hidden Markov models [representing, e.g., Matthew’s use of Mark], to various independence tests on contingency tables, sometimes bringing into the model an extra source denoted by Q. Including some R code for hidden Markov models. Once again, from my outsider viewpoint, this fragmented approach to the problem sounds problematic and inconclusive. And rather verbose in extensive discussions of descriptive statistics. Not that I was expecting a sudden Monty Python-like ray of light and booming voice to disclose the truth! Or that I crave for more p-values (some may be found hiding within the book). But I still wonder about the phylogeny… Especially since phylogenies are used in text authentication as pointed out to me by Robin Ryder for Chauncer’s Canterbury Tales.

A kind message I received from the University of Canterbury, Christchurch:

On behalf of the University of Canterbury, best wishes for the festive season.

This year has been challenging for UC, as it has for many organisations. But with work on campus remediation well and truly underway, a busy enrolment period and a recent commitment from the Government to support us in our recovery, we can take heart that the University is making good progress following the events of the past couple of years.

We believe that a strong university goes hand in hand with a robust, cohesive and growing economy and community. We are committed to supporting the recovery of Christchurch through closer partnerships with the business sector, secondary schools, Ngai Tahu, partner institutions, other tertiary education providers and crown research institutes.

Another important component of our plan for the future is a commitment to engage in Christchurch´s new central city health precinct, reflecting our vision of a university that isn’t just a place students come to when they want a degree; but a university that is a learning environment well connected with its communities.

I hope you will have the opportunity over the holiday period to relax, reflect on the year and look ahead.

Please accept my personal thanks for your interest in and support for the University this year. It has made a difference.

Thanks to a link on R-bloggers, I was introduced to Luis Apiolaza’s blog, Quantum Forest, which covers data analyses and R comments he encounters in his research as a quantitative forester/geneticist. And he works at the University of Canterbury, Christchurch, where I first taught from Bayesian Core in 2006. Which may be why he chose Bayesian Core as one of the three books he is currently reading to understand Bayesian statistics better. (The other two are Jim Albert’s Bayesian computation with R, and Bill Bolstad’s Introduction to Bayesian Statistics, which is not the one I reviewed recently.) Luis has just started the book but he mentions that “the book has managed to capture my interest”, which is real nice, and being annoyed by the self-contained label we put on the back cover. Which is a reaction I also got from some students when teaching the book for a week in Australia, as they thought they could take it without a probability background. Hopefully, we’ll manage to complete our revision before next summer!

An update on the 5th: the damages are only material and no clear structural damage seems to have imperilled the buildings, but watching the million or so books off their shelves below means there is a substantial effort ahead! (Emails to math.canterbury.ac.nz are still bouncing back…)

This one-before-last day at València 9 was fairly busy and I skipped the [tantalising] trip back to Sella to attend morning and afternoon talks. The first session involved Nicolas Chopin and Pierre Jacob’s free-energy paper whose earlier version I had heard at CREST, which builds on the earlier paper of Nicolas with Tony Lelièvre and Gabriel Stoltz to build a sequential Monte Carlo sampler that is biased along a preferential direction in order to fight multimodality and label switching in the case of mixtures. Peter Green rightly pointed out the difficulty in building this direction, which appears like a principal component to me, but this may open a new direction for research on a potentially adaptive direction updated with the SMC sampler… Although I always have trouble understanding the gist of causal models, Thomas Richardson’s talk about transparent parameterisation was quite interesting in its links both with contingency tables and with identifiability issues (should Bayesians care about identifiability?! I did not really understand why the data could help in specifying the unidentified parameter in an empirical Bayes manner, though).

The morning talk by Darren Wilkinson was a particularly enticing talk in that Darren presented in a very articulate manner the specifics of analysing stochastic kinetic models for bacterial regulation and that he also introduced a likelihood-free MCMC that was not ABC-MCMC. (At first sight, it sounds like the auxiliary variable technique of Møller, Pettit, Reeves and Berthelsen, but I want to read the paper to understand better the differences.) Despite the appalling audio and video rendering in the conference room, the filmed discussion by Samuel Kou got into a comparison with ABC. The afternoon non-parametric session left me a bit confused as to the infinite regress on Dirichlet process expansions, but I enjoyed the next talk by Geoff Nicholls on partial ordering inference immensely, even though I missed the bishop example at the beginning because the talks got drifted due to the absence of the first speaker of the session. During the poster session (where again I only saw a fourth of the material!), I had the pleasant surprise to meet a student from the University of Canterbury, Christchurch, who took my Bayesian Core class when I visited in 2006.

I have received the following announcement for a continuing (tenured) position in Statistics, at the level of Senior Lecturer or Associate Professor (comparable to Associate Professor/Professor, respectively, in the US system) in the Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.

“We are looking for an individual with a strong demonstrated background in research, teaching and professional practice. In this position, the successful candidate will contribute to the Departments undergraduate and postgraduate teaching and undertake high quality research building excellent collaborative relationships with other appropriate researchers, locally, nationally and internationally.

This position will also have the outstanding opportunity to establish and lead a Statistical Consulting Unit for the University of Canterbury. Hosted by the Department of Mathematics and Statistics, this Unit will provide statistical advice and support services to meet the needs of
postgraduate students and staff across the University.”